文章摘要
毛东兴,王勇,姜在秀.车内噪声品质低沉度参量的数学模型[J].声学技术,2006,(6):533~539
车内噪声品质低沉度参量的数学模型
Parametric model for low-frequency index of subjective car interior sound quality
投稿时间:2006-08-31  修订日期:2006-10-30
DOI:
中文关键词: 声品质参量  低频成分  主观参量模型
英文关键词: sound quality descriptor  low-frequency contents  subjective model  car interior noise
基金项目:This work was supported by National Natural Science Foundation of China (No. 10374071)MAO Dongxing, Associate Professor; Research interests: psychoacoustics and sound quality,environmental acoustics and noise control.
作者单位
毛东兴 同济大学声学研究所, 上海, 200092 
王勇 重庆汽车研究所, 重庆, 400039 
姜在秀 同济大学声学研究所, 上海, 200092 
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中文摘要:
      低频成分的噪声是车内噪声的主要特征之一,并对车辆的力度、轰鸣、烦恼等主观听觉感知特征产生影响,本文的研究旨在建立车内低频噪声的主观声品质评价的参量和数学模型。首先采用听音和想象法等词汇描述进行主观问卷调查,通过对调查得到描述词汇的统计分析,得到了描述车内噪声低频特征的中文描述词-低沉。然后采用仿真头双耳记录的车内噪声信号进行实验室主观评价,通过对评价结果的分析得到了影响低沉度感知的主要参量,并由此建立了以1/3倍频程声压级、锐度和粗糙度为变量的低沉度参量的数学模型。采用成对比较法和语义细分法主观评价实验的结果验证了模型的准确性。结果表明,低沉度模型的预测结果与主观评价结果具有很高的相关性,从而证明了所提出模型的有效性。
英文摘要:
      Low-frequency character is one of the most important properties for car interior noise quality, subjective sensation of this kind of sound character is usually expressed in the context of powerful, booming, annoying, and/or even offensive as indicated in some literatures. This study is to establish an index for sound quality focused on subjective perception of car interior noise with low frequency contents. In order to obtain an appropriate verbal descriptor for this feature, questionnaire for verbal description through subjective listening and imaging of sounds are carried out. A verbal expression "Dichen" in Chinese was concluded from statistical analysis of subjective listening data. Jury tests for indicator "Dichen" was performed by using binaurally recorded car interior noise as stimuli; subjective results are analyzed and correlated with objective quantities. Mathematical model for "Dichen" is developed as a function of 1/3 octave sound pressure levels, sharpness and roughness quantities. The proposed model was verified by data collected from subjective assessment with paired comparison and semantic differential methods. Results indicate a strong correlation between model predicted and subjective data, and therefore proved the developed model an effective index for subjective low-frequency perception.
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